# Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?

arXiv: https://arxiv.org/abs/2209.12320 · HTML: https://mlsanity.com/paper/2209.12320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 25 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Entailment as Few-Shot Learner](https://mlsanity.com/paper/2104.14690/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documents](https://mlsanity.com/paper/2302.09324/) (2023)

## Abstract (arXiv)

Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern. Research into online sexual grooming has often relied on domain experts to manually annotate conversations, limiting both scale and scope. In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. Informed by psychological theories of online grooming, we label 6772 chat messages sent by child-sex offenders with one of eleven predatory behaviors. We train bag-of-words and natural language inference models to classify each behavior, and show that the best performing models classify behaviors in a manner that is consistent, but not on-par, with human annotation.